1. Field of Disclosure
The disclosure generally relates to the field of tracking motion of a system, and more specifically, to hand shape classification from visual input.
2. Description of the Related Art
There has been a growing interest in capturing and recognizing hand shapes because of its broad application. The recognized hand shape can be used to transfer hand motion to robot systems (e.g., teleoperation, telemanipulation), to implement pervasive user interface, and to detect specific hand movements.
One conventional approach to capture hand movements instruments the human demonstrator with a data glove. While the human demonstrator performs certain tasks, sensors attached to the data glove measure the articulation angles or the Cartesian positions of selected feature points on the glove. See S. Ekvall and D. Kragic, “Grasp recognition for programming by demonstration”, Int. Conf Robotics and Automation (ICRA), 748-753 (2005), the content of which is incorporated by reference herein in its entirety. Although measurement of the glove configuration captures the underlying hand movement, the glove often obstructs the demonstrators contact with the object and may prevent natural hand movements. Moreover, calibration and adjustments for proper fit for different size hands is required to ensure accurate measurements.
Another conventional approach, in lieu of using a data glove, places markers on the hands of the human demonstrator and records hand articulations by tracking the positions of the markers. See N. Pollard and V. B. Zordan, “Physically based grasping control from examples”, AMC SIGGRAPH/Eurographics Symp. On Computer Animation, 311-318 (2005); see also L. Chang, N. Pollard, T. Mitchell, and E. Xing, “Feature selection for grasp recognition from optical markers”, Intelligent Robots and Systems (IROS), 2944-2950 (2007), both of which are incorporated by reference herein in their entirety. To minimize the effects of marker occlusions, multiple video cameras are used to track the markers. This approach is time consuming and requires considerable calibration in an instrumented and controlled environment.
Various approaches have also been developed for hand posture recognition. See Y. Wu and T. S. Huang, “Vision-Based Gesture Recognition: A Review”, Lecture Notes in Computer Science, 1739-103 (1999), the content of which is incorporated by reference herein in its entirety. For example, there are approaches that deal with view-invariance (See Y. Wu and T. S. Huang, “View-Independent Recognition of Hand Postures”, (2000), the content of which is incorporated by reference herein in its entirety), recognition under complex backgrounds (See J. Triesch and C. von der Malsburg, “A System for Person-Independent Hand Posture Recognition against Complex Backgrounds”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1449-1453 (2001), the content of which is incorporated by reference herein in its entirety), and adaptive learning using SIFT features (See C. Wang and K. Wang, “Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction”, LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES, 370-317 (2008), the content of which is incorporated by reference herein in its entirety). However, these approaches are insufficient because their outcomes are largely subjective to viewing conditions such as lighting, blur variation, and view changes.
Hence, there is lacking, inter alia, a system and method for efficiently and accurately capturing and recognizing hand postures in real time.